The project attempts to produce a theory of storage in and retrieval from human memory, using a Bayesian-based modeling approach (termed REM), rooted in principles of optimized processes operating in a cognitive environment constrained by capacity limitations. The approach will involve modeling of data collected from a wide variety of tasks emphasizing different domains of cognition; in each testing of recognition and recall using free response and signal-to-respond will be the normative experimental method. The domains include: 1) Episodic memory (recognition, associative recognition, cued recall, free recall), emphasizing the role of context in storage and retrieval. 2) Knowledge retrieval. 3) Implicit memory and long-term priming. These latter two domains will include studies of naming, perceptual identification, lexical decision, production of associates, animacy judgments, stem- and fragment-completion. 3) Short-term priming (negative priming). 4) Perceptual benefits due to priming (both long- and short-term). 5)Visual search (to establish one key capacity limitation and its nature). To develop the theory, we will explore response time and accuracy (and their relation) as measures of underlying processing mechanisms. We shall also develop means to replace the currently abstract features of the REM modeling framework with 'real' features obtained through new forms of data analysis.